Typically, a customer may wish to converse with a customer support representative of an enterprise to enquire about products/services of interest, to resolve concerns, to make payments, to lodge complaints, and the like. To serve such a purpose, the enterprises may deploy both, live and automated conversational agents to interact with the customers and provide them with desired assistance.
Generally, when a customer contacts customer support using speech or text interface, the first interaction takes place with an automated conversational agent, also referred to herein as a Virtual Agent (VA). The VA may use Natural Language Processing (NLP) algorithms and special grammar to interpret customer's natural language inputs, whether provided in a spoken form or a textual form and respond appropriately.
Customers seeking assistance from customer support representatives may pose a variety of queries to the VAs. Furthermore, queries with similar intentions may be framed differently by different customers. In many example scenarios, a VA may be limited in its ability to provide assistance to some customers on account of a sheer variety of requests that the VA has to interpret and thereafter accomplish tasks to service those requests. In some scenarios, if the VA is not being able to interpret a customer query correctly, it may be configured to request the customer to ask the query in a different form or to end the conversation and deflect the rest of the conversation to a human agent.
Such an approach has several shortcomings. For example, it is observed that a large number of conversations get handed over to human agents, who are trained to take the conversation to a graceful closure. As a result of such deflections, time spent on conversations by human agents increases substantially, which may not be preferred by the enterprises.
Further, as explained above, in some scenarios the VA may be configured to request the customer to ask the question in a different form if the customer query is not clear. In some scenarios, the query disambiguation may take longer and the customer may not get the best experience in a timely manner.